Overview

Dataset statistics

Number of variables27
Number of observations8.897
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory216.0 B

Variable types

Numeric20
Categorical7

Alerts

nation has a high cardinality: 159 distinct values High cardinality
league has a high cardinality: 71 distinct values High cardinality
team has a high cardinality: 1049 distinct values High cardinality
red_card_continent is highly correlated with second_yellow_card_continentHigh correlation
age is highly correlated with yellow_card_champHigh correlation
goals_selection is highly correlated with selections_nationHigh correlation
selections_nation is highly correlated with goals_selectionHigh correlation
price is highly correlated with goal_champ and 2 other fieldsHigh correlation
goal_champ is highly correlated with price and 3 other fieldsHigh correlation
assist_champ is highly correlated with price and 4 other fieldsHigh correlation
yellow_card_champ is highly correlated with age and 4 other fieldsHigh correlation
second_yellow_card_champ is highly correlated with yellow_card_champHigh correlation
goal_cup is highly correlated with goal_champ and 3 other fieldsHigh correlation
assist_cup is highly correlated with assist_champ and 2 other fieldsHigh correlation
yellow_card_cup is highly correlated with goal_cup and 1 other fieldsHigh correlation
goal_continent is highly correlated with assist_continent and 1 other fieldsHigh correlation
assist_continent is highly correlated with goal_continent and 1 other fieldsHigh correlation
yellow_card_continent is highly correlated with goal_continent and 1 other fieldsHigh correlation
second_yellow_card_continent is highly correlated with red_card_continentHigh correlation
red_card_continent is highly correlated with second_yellow_card_continentHigh correlation
goals_selection is highly correlated with selections_nation and 3 other fieldsHigh correlation
selections_nation is highly correlated with goals_selectionHigh correlation
goal_champ is highly correlated with goals_selection and 4 other fieldsHigh correlation
assist_champ is highly correlated with goal_champ and 4 other fieldsHigh correlation
yellow_card_champ is highly correlated with goal_champ and 2 other fieldsHigh correlation
second_yellow_card_champ is highly correlated with yellow_card_champHigh correlation
goal_cup is highly correlated with goal_champ and 1 other fieldsHigh correlation
assist_cup is highly correlated with assist_champ and 2 other fieldsHigh correlation
yellow_card_cup is highly correlated with assist_cupHigh correlation
goal_continent is highly correlated with goals_selection and 4 other fieldsHigh correlation
assist_continent is highly correlated with goals_selection and 3 other fieldsHigh correlation
yellow_card_continent is highly correlated with goal_continent and 1 other fieldsHigh correlation
second_yellow_card_continent is highly correlated with red_card_continentHigh correlation
red_card_continent is highly correlated with second_yellow_card_continentHigh correlation
goals_selection is highly correlated with selections_nationHigh correlation
selections_nation is highly correlated with goals_selectionHigh correlation
goal_champ is highly correlated with assist_champ and 1 other fieldsHigh correlation
assist_champ is highly correlated with goal_champ and 1 other fieldsHigh correlation
yellow_card_champ is highly correlated with goal_champ and 1 other fieldsHigh correlation
goal_cup is highly correlated with assist_cup and 1 other fieldsHigh correlation
assist_cup is highly correlated with goal_cup and 1 other fieldsHigh correlation
yellow_card_cup is highly correlated with goal_cup and 1 other fieldsHigh correlation
goal_continent is highly correlated with assist_continent and 1 other fieldsHigh correlation
assist_continent is highly correlated with goal_continent and 1 other fieldsHigh correlation
yellow_card_continent is highly correlated with goal_continent and 1 other fieldsHigh correlation
second_yellow_card_continent is highly correlated with red_card_continentHigh correlation
Unnamed: 0 is highly correlated with leagueHigh correlation
red_card_continent is highly correlated with selections_nation and 1 other fieldsHigh correlation
age is highly correlated with selections_nationHigh correlation
league is highly correlated with Unnamed: 0High correlation
goals_selection is highly correlated with selections_nation and 6 other fieldsHigh correlation
selections_nation is highly correlated with red_card_continent and 5 other fieldsHigh correlation
price is highly correlated with goals_selection and 2 other fieldsHigh correlation
goal_champ is highly correlated with goals_selection and 4 other fieldsHigh correlation
assist_champ is highly correlated with goals_selection and 7 other fieldsHigh correlation
yellow_card_champ is highly correlated with assist_champ and 3 other fieldsHigh correlation
second_yellow_card_champ is highly correlated with yellow_card_champ and 1 other fieldsHigh correlation
goal_cup is highly correlated with goals_selection and 4 other fieldsHigh correlation
assist_cup is highly correlated with assist_champ and 3 other fieldsHigh correlation
yellow_card_cup is highly correlated with yellow_card_champ and 2 other fieldsHigh correlation
goal_continent is highly correlated with goals_selection and 7 other fieldsHigh correlation
assist_continent is highly correlated with goals_selection and 8 other fieldsHigh correlation
yellow_card_continent is highly correlated with assist_champ and 5 other fieldsHigh correlation
second_yellow_card_continent is highly correlated with red_card_continentHigh correlation
goal_continent is highly skewed (γ1 = 21.52801949) Skewed
Unnamed: 0 has unique values Unique
red_card_continent has 8415 (94.6%) zeros Zeros
goals_selection has 7513 (84.4%) zeros Zeros
selections_nation has 5052 (56.8%) zeros Zeros
goal_champ has 3009 (33.8%) zeros Zeros
assist_champ has 3280 (36.9%) zeros Zeros
own_goal_champ has 8157 (91.7%) zeros Zeros
yellow_card_champ has 1303 (14.6%) zeros Zeros
second_yellow_card_champ has 6616 (74.4%) zeros Zeros
red_card_champ has 6511 (73.2%) zeros Zeros
goal_cup has 6443 (72.4%) zeros Zeros
assist_cup has 6662 (74.9%) zeros Zeros
yellow_card_cup has 6189 (69.6%) zeros Zeros
red_card_cup has 7803 (87.7%) zeros Zeros
goal_continent has 7581 (85.2%) zeros Zeros
assist_continent has 7608 (85.5%) zeros Zeros
yellow_card_continent has 7234 (81.3%) zeros Zeros
second_yellow_card_continent has 8285 (93.1%) zeros Zeros

Reproduction

Analysis started2022-05-29 21:00:13.112120
Analysis finished2022-05-29 21:01:31.490580
Duration1 minute and 18.38 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct8897
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6415.278746
Minimum1
Maximum12705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:31.638412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile660.8
Q13220
median6469
Q39617
95-th percentile12083.4
Maximum12705
Range12704
Interquartile range (IQR)6397

Descriptive statistics

Standard deviation3672.186079
Coefficient of variation (CV)0.5724125521
Kurtosis-1.209563134
Mean6415.278746
Median Absolute Deviation (MAD)3195
Skewness-0.02242182128
Sum57076735
Variance13484950.6
MonotonicityNot monotonic
2022-05-29T23:01:31.949702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10191
 
< 0.1%
11531
 
< 0.1%
26611
 
< 0.1%
82841
 
< 0.1%
92151
 
< 0.1%
45421
 
< 0.1%
108721
 
< 0.1%
103361
 
< 0.1%
42431
 
< 0.1%
10431
 
< 0.1%
Other values (8887)8887
99.9%
ValueCountFrequency (%)
11
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
81
< 0.1%
111
< 0.1%
121
< 0.1%
151
< 0.1%
171
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
127051
< 0.1%
127041
< 0.1%
127031
< 0.1%
127021
< 0.1%
127011
< 0.1%
126991
< 0.1%
126981
< 0.1%
126971
< 0.1%
126961
< 0.1%
126951
< 0.1%

red_card_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2498595032
Minimum0
Maximum59
Zeros8415
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:32.198268image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum59
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.860633278
Coefficient of variation (CV)7.446718074
Kurtosis301.0397492
Mean0.2498595032
Median Absolute Deviation (MAD)0
Skewness14.50689042
Sum2223
Variance3.461956197
MonotonicityNot monotonic
2022-05-29T23:01:32.396655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
08415
94.6%
1206
 
2.3%
267
 
0.8%
344
 
0.5%
429
 
0.3%
523
 
0.3%
717
 
0.2%
812
 
0.1%
610
 
0.1%
99
 
0.1%
Other values (22)65
 
0.7%
ValueCountFrequency (%)
08415
94.6%
1206
 
2.3%
267
 
0.8%
344
 
0.5%
429
 
0.3%
523
 
0.3%
610
 
0.1%
717
 
0.2%
812
 
0.1%
99
 
0.1%
ValueCountFrequency (%)
591
 
< 0.1%
551
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
341
 
< 0.1%
291
 
< 0.1%
272
< 0.1%
242
< 0.1%
233
< 0.1%
221
 
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.0524896
Minimum16
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:32.590578image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile19
Q123
median26
Q329
95-th percentile34
Maximum46
Range30
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.561708285
Coefficient of variation (CV)0.1750968278
Kurtosis-0.2249090852
Mean26.0524896
Median Absolute Deviation (MAD)3
Skewness0.4142616571
Sum231789
Variance20.80918248
MonotonicityNot monotonic
2022-05-29T23:01:32.788887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
25748
 
8.4%
23722
 
8.1%
24697
 
7.8%
27672
 
7.6%
26669
 
7.5%
22661
 
7.4%
28620
 
7.0%
21566
 
6.4%
29556
 
6.2%
30455
 
5.1%
Other values (19)2531
28.4%
ValueCountFrequency (%)
1610
 
0.1%
1737
 
0.4%
18160
 
1.8%
19306
3.4%
20442
5.0%
21566
6.4%
22661
7.4%
23722
8.1%
24697
7.8%
25748
8.4%
ValueCountFrequency (%)
463
 
< 0.1%
441
 
< 0.1%
423
 
< 0.1%
417
 
0.1%
408
 
0.1%
3922
 
0.2%
3836
 
0.4%
3766
0.7%
3697
1.1%
35138
1.6%

nation
Categorical

HIGH CARDINALITY

Distinct159
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
Brazil
 
612
Japan
 
575
Argentina
 
427
Iran
 
365
Mexico
 
242
Other values (154)
6676 

Length

Max length22
Median length18
Mean length7.263459593
Min length4

Characters and Unicode

Total characters64.623
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.3%

Sample

1st rowTunisia
2nd rowCzechRepublic
3rd rowCanada
4th rowBelgium
5th rowUkraine

Common Values

ValueCountFrequency (%)
Brazil612
 
6.9%
Japan575
 
6.5%
Argentina427
 
4.8%
Iran365
 
4.1%
Mexico242
 
2.7%
Colombia240
 
2.7%
UnitedStates210
 
2.4%
Egypt210
 
2.4%
France202
 
2.3%
Algeria199
 
2.2%
Other values (149)5615
63.1%

Length

2022-05-29T23:01:33.070229image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brazil612
 
6.9%
japan575
 
6.5%
argentina427
 
4.8%
iran365
 
4.1%
mexico242
 
2.7%
colombia240
 
2.7%
unitedstates210
 
2.4%
egypt210
 
2.4%
france202
 
2.3%
algeria199
 
2.2%
Other values (149)5615
63.1%

Most occurring characters

ValueCountFrequency (%)
a9429
14.6%
n5469
 
8.5%
i5405
 
8.4%
e4994
 
7.7%
r4718
 
7.3%
o3055
 
4.7%
t2949
 
4.6%
l2785
 
4.3%
u2029
 
3.1%
g1815
 
2.8%
Other values (46)21975
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54392
84.2%
Uppercase Letter10026
 
15.5%
Other Punctuation167
 
0.3%
Decimal Number36
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a9429
17.3%
n5469
10.1%
i5405
9.9%
e4994
9.2%
r4718
8.7%
o3055
 
5.6%
t2949
 
5.4%
l2785
 
5.1%
u2029
 
3.7%
g1815
 
3.3%
Other values (17)11744
21.6%
Uppercase Letter
ValueCountFrequency (%)
S1250
12.5%
A1130
11.3%
C933
 
9.3%
B824
 
8.2%
I807
 
8.0%
U690
 
6.9%
J586
 
5.8%
M507
 
5.1%
R418
 
4.2%
T409
 
4.1%
Other values (14)2472
24.7%
Other Punctuation
ValueCountFrequency (%)
,130
77.8%
'37
 
22.2%
Decimal Number
ValueCountFrequency (%)
036
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64418
99.7%
Common205
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9429
14.6%
n5469
 
8.5%
i5405
 
8.4%
e4994
 
7.8%
r4718
 
7.3%
o3055
 
4.7%
t2949
 
4.6%
l2785
 
4.3%
u2029
 
3.1%
g1815
 
2.8%
Other values (41)21770
33.8%
Common
ValueCountFrequency (%)
,130
63.4%
'37
 
18.0%
036
 
17.6%
(1
 
0.5%
)1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII64622
> 99.9%
None1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a9429
14.6%
n5469
 
8.5%
i5405
 
8.4%
e4994
 
7.7%
r4718
 
7.3%
o3055
 
4.7%
t2949
 
4.6%
l2785
 
4.3%
u2029
 
3.1%
g1815
 
2.8%
Other values (45)21974
34.0%
None
ValueCountFrequency (%)
ä1
100.0%

league
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct71
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
PremierLeague
 
547
SuperLeague
 
388
J2League
 
387
PrimeraDivisión
 
373
Liga1
 
288
Other values (66)
6914 

Length

Max length32
Median length19
Mean length10.80229291
Min length3

Characters and Unicode

Total characters96.108
Distinct characters58
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowLigue1
2nd rowFortunaLiga
3rd rowSuperLiga
4th rowJupilerProLeague
5th rowPremierLiga

Common Values

ValueCountFrequency (%)
PremierLeague547
 
6.1%
SuperLeague388
 
4.4%
J2League387
 
4.3%
PrimeraDivisión373
 
4.2%
Liga1288
 
3.2%
Bundesliga284
 
3.2%
PremierLiga234
 
2.6%
ABSAPremiership225
 
2.5%
MLS219
 
2.5%
J1League219
 
2.5%
Other values (61)5733
64.4%

Length

2022-05-29T23:01:33.308340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
premierleague547
 
6.1%
superleague388
 
4.4%
j2league387
 
4.3%
primeradivisión373
 
4.2%
liga1288
 
3.2%
bundesliga284
 
3.2%
superliga270
 
3.0%
premierliga234
 
2.6%
absapremiership225
 
2.5%
mls219
 
2.5%
Other values (60)5682
63.9%

Most occurring characters

ValueCountFrequency (%)
e13751
14.3%
a9694
 
10.1%
i9025
 
9.4%
r7841
 
8.2%
g6502
 
6.8%
u6289
 
6.5%
L6173
 
6.4%
s3467
 
3.6%
P3033
 
3.2%
n2906
 
3.0%
Other values (48)27427
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter73777
76.8%
Uppercase Letter20355
 
21.2%
Decimal Number1659
 
1.7%
Other Punctuation316
 
0.3%
Other Letter1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L6173
30.3%
P3033
14.9%
S2425
 
11.9%
A1643
 
8.1%
B892
 
4.4%
J754
 
3.7%
C595
 
2.9%
D594
 
2.9%
M540
 
2.7%
N522
 
2.6%
Other values (16)3184
15.6%
Lowercase Letter
ValueCountFrequency (%)
e13751
18.6%
a9694
13.1%
i9025
12.2%
r7841
10.6%
g6502
8.8%
u6289
8.5%
s3467
 
4.7%
n2906
 
3.9%
l2772
 
3.8%
o2407
 
3.3%
Other values (15)9123
12.4%
Decimal Number
ValueCountFrequency (%)
11087
65.5%
2457
27.5%
0115
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.216
68.4%
'98
31.0%
/2
 
0.6%
Other Letter
ValueCountFrequency (%)
ª1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin94133
97.9%
Common1975
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e13751
14.6%
a9694
 
10.3%
i9025
 
9.6%
r7841
 
8.3%
g6502
 
6.9%
u6289
 
6.7%
L6173
 
6.6%
s3467
 
3.7%
P3033
 
3.2%
n2906
 
3.1%
Other values (42)25452
27.0%
Common
ValueCountFrequency (%)
11087
55.0%
2457
23.1%
.216
 
10.9%
0115
 
5.8%
'98
 
5.0%
/2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII95012
98.9%
None1096
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e13751
14.5%
a9694
 
10.2%
i9025
 
9.5%
r7841
 
8.3%
g6502
 
6.8%
u6289
 
6.6%
L6173
 
6.5%
s3467
 
3.6%
P3033
 
3.2%
n2906
 
3.1%
Other values (43)26331
27.7%
None
ValueCountFrequency (%)
ó373
34.0%
é342
31.2%
Á195
17.8%
ü185
16.9%
ª1
 
0.1%

team
Categorical

HIGH CARDINALITY

Distinct1049
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
TokushimaVort.
 
30
TochigiSC
 
28
KashiwaReysol
 
27
Nacional
 
26
TavuaFC
 
25
Other values (1044)
8761 

Length

Max length24
Median length14
Mean length9.781948972
Min length3

Characters and Unicode

Total characters87.030
Distinct characters83
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.4%

Sample

1st rowOGCNice
2nd row1.FKPribram
3rd rowRedStar
4th rowClubBrugge
5th rowChornomorets

Common Values

ValueCountFrequency (%)
TokushimaVort.30
 
0.3%
TochigiSC28
 
0.3%
KashiwaReysol27
 
0.3%
Nacional26
 
0.3%
TavuaFC25
 
0.3%
AlbirexNiigata25
 
0.3%
KagoshimaUtd.25
 
0.3%
FajrSepasi23
 
0.3%
Petrojet23
 
0.3%
V0V.Nagasaki22
 
0.2%
Other values (1039)8643
97.1%

Length

2022-05-29T23:01:33.504421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tokushimavort30
 
0.3%
tochigisc28
 
0.3%
kashiwareysol27
 
0.3%
nacional26
 
0.3%
tavuafc25
 
0.3%
albirexniigata25
 
0.3%
kagoshimautd25
 
0.3%
petrojet23
 
0.3%
fajrsepasi23
 
0.3%
v0v.nagasaki22
 
0.2%
Other values (1039)8643
97.1%

Most occurring characters

ValueCountFrequency (%)
a9979
 
11.5%
e5980
 
6.9%
i5422
 
6.2%
o5165
 
5.9%
n5029
 
5.8%
r4949
 
5.7%
l3658
 
4.2%
s3503
 
4.0%
t3430
 
3.9%
C2816
 
3.2%
Other values (73)37099
42.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter65220
74.9%
Uppercase Letter20254
 
23.3%
Other Punctuation997
 
1.1%
Decimal Number482
 
0.6%
Close Punctuation25
 
< 0.1%
Open Punctuation25
 
< 0.1%
Control17
 
< 0.1%
Final Punctuation10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a9979
15.3%
e5980
 
9.2%
i5422
 
8.3%
o5165
 
7.9%
n5029
 
7.7%
r4949
 
7.6%
l3658
 
5.6%
s3503
 
5.4%
t3430
 
5.3%
u2313
 
3.5%
Other values (30)15792
24.2%
Uppercase Letter
ValueCountFrequency (%)
C2816
13.9%
S2163
 
10.7%
F1850
 
9.1%
A1805
 
8.9%
B1049
 
5.2%
M980
 
4.8%
P944
 
4.7%
K783
 
3.9%
R729
 
3.6%
G708
 
3.5%
Other values (19)6427
31.7%
Decimal Number
ValueCountFrequency (%)
0332
68.9%
164
 
13.3%
928
 
5.8%
519
 
3.9%
218
 
3.7%
612
 
2.5%
49
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.944
94.7%
'34
 
3.4%
&19
 
1.9%
Close Punctuation
ValueCountFrequency (%)
)25
100.0%
Open Punctuation
ValueCountFrequency (%)
(25
100.0%
Control
ValueCountFrequency (%)
17
100.0%
Final Punctuation
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin85474
98.2%
Common1556
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9979
 
11.7%
e5980
 
7.0%
i5422
 
6.3%
o5165
 
6.0%
n5029
 
5.9%
r4949
 
5.8%
l3658
 
4.3%
s3503
 
4.1%
t3430
 
4.0%
C2816
 
3.3%
Other values (59)35543
41.6%
Common
ValueCountFrequency (%)
.944
60.7%
0332
 
21.3%
164
 
4.1%
'34
 
2.2%
928
 
1.8%
)25
 
1.6%
(25
 
1.6%
&19
 
1.2%
519
 
1.2%
218
 
1.2%
Other values (4)48
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII86281
99.1%
None739
 
0.8%
Punctuation10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a9979
 
11.6%
e5980
 
6.9%
i5422
 
6.3%
o5165
 
6.0%
n5029
 
5.8%
r4949
 
5.7%
l3658
 
4.2%
s3503
 
4.1%
t3430
 
4.0%
C2816
 
3.3%
Other values (55)36350
42.1%
None
ValueCountFrequency (%)
é188
25.4%
ó101
13.7%
á96
13.0%
ö52
 
7.0%
è52
 
7.0%
ú49
 
6.6%
ü45
 
6.1%
í41
 
5.5%
ï29
 
3.9%
ã21
 
2.8%
Other values (7)65
 
8.8%
Punctuation
ValueCountFrequency (%)
10
100.0%

goals_selection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5997527256
Minimum0
Maximum85
Zeros7513
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:33.728654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum85
Range85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.642641972
Coefficient of variation (CV)4.406219195
Kurtosis206.6966866
Mean0.5997527256
Median Absolute Deviation (MAD)0
Skewness11.05196816
Sum5336
Variance6.983556594
MonotonicityNot monotonic
2022-05-29T23:01:33.990657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
07513
84.4%
1560
 
6.3%
2252
 
2.8%
3156
 
1.8%
4122
 
1.4%
558
 
0.7%
642
 
0.5%
732
 
0.4%
921
 
0.2%
819
 
0.2%
Other values (27)122
 
1.4%
ValueCountFrequency (%)
07513
84.4%
1560
 
6.3%
2252
 
2.8%
3156
 
1.8%
4122
 
1.4%
558
 
0.7%
642
 
0.5%
732
 
0.4%
819
 
0.2%
921
 
0.2%
ValueCountFrequency (%)
851
< 0.1%
601
< 0.1%
501
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
361
< 0.1%
341
< 0.1%
312
< 0.1%
301
< 0.1%

selections_nation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct126
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.787793638
Minimum0
Maximum176
Zeros5052
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:34.232137image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile32
Maximum176
Range176
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.98991475
Coefficient of variation (CV)2.589918662
Kurtosis28.25345793
Mean5.787793638
Median Absolute Deviation (MAD)0
Skewness4.687896966
Sum51494
Variance224.6975443
MonotonicityNot monotonic
2022-05-29T23:01:34.484123image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05052
56.8%
1631
 
7.1%
2457
 
5.1%
3363
 
4.1%
4237
 
2.7%
5233
 
2.6%
6168
 
1.9%
7142
 
1.6%
9130
 
1.5%
8130
 
1.5%
Other values (116)1354
 
15.2%
ValueCountFrequency (%)
05052
56.8%
1631
 
7.1%
2457
 
5.1%
3363
 
4.1%
4237
 
2.7%
5233
 
2.6%
6168
 
1.9%
7142
 
1.6%
8130
 
1.5%
9130
 
1.5%
ValueCountFrequency (%)
1761
< 0.1%
1671
< 0.1%
1592
< 0.1%
1541
< 0.1%
1522
< 0.1%
1472
< 0.1%
1421
< 0.1%
1361
< 0.1%
1341
< 0.1%
1332
< 0.1%

position
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
Goalkeeper
1701 
CentralMidfield
1452 
DefensiveMidfield
1381 
AttackingMidfield
1101 
RightWinger
917 
Other values (7)
2345 

Length

Max length17
Median length15
Mean length13.01562324
Min length7

Characters and Unicode

Total characters115.800
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoalkeeper
2nd rowLeftWinger
3rd rowGoalkeeper
4th rowSecondStriker
5th rowLeftMidfield

Common Values

ValueCountFrequency (%)
Goalkeeper1701
19.1%
CentralMidfield1452
16.3%
DefensiveMidfield1381
15.5%
AttackingMidfield1101
12.4%
RightWinger917
10.3%
LeftWinger889
10.0%
LeftMidfield317
 
3.6%
Midfielder316
 
3.6%
RightMidfield303
 
3.4%
SecondStriker188
 
2.1%
Other values (2)332
 
3.7%

Length

2022-05-29T23:01:34.700603image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
goalkeeper1701
19.1%
centralmidfield1452
16.3%
defensivemidfield1381
15.5%
attackingmidfield1101
12.4%
rightwinger917
10.3%
leftwinger889
10.0%
leftmidfield317
 
3.6%
midfielder316
 
3.6%
rightmidfield303
 
3.4%
secondstriker188
 
2.1%
Other values (2)332
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e19776
17.1%
i15436
13.3%
d10260
 
8.9%
l8023
 
6.9%
f7625
 
6.6%
t6268
 
5.4%
r6147
 
5.3%
n6096
 
5.3%
M4870
 
4.2%
a4418
 
3.8%
Other values (18)26881
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100355
86.7%
Uppercase Letter15445
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e19776
19.7%
i15436
15.4%
d10260
10.2%
l8023
8.0%
f7625
 
7.6%
t6268
 
6.2%
r6147
 
6.1%
n6096
 
6.1%
a4418
 
4.4%
g4127
 
4.1%
Other values (8)12179
12.1%
Uppercase Letter
ValueCountFrequency (%)
M4870
31.5%
W1806
 
11.7%
G1701
 
11.0%
D1549
 
10.0%
C1452
 
9.4%
R1220
 
7.9%
L1206
 
7.8%
A1101
 
7.1%
S376
 
2.4%
F164
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin115800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e19776
17.1%
i15436
13.3%
d10260
 
8.9%
l8023
 
6.9%
f7625
 
6.6%
t6268
 
5.4%
r6147
 
5.3%
n6096
 
5.3%
M4870
 
4.2%
a4418
 
3.8%
Other values (18)26881
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII115800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e19776
17.1%
i15436
13.3%
d10260
 
8.9%
l8023
 
6.9%
f7625
 
6.6%
t6268
 
5.4%
r6147
 
5.3%
n6096
 
5.3%
M4870
 
4.2%
a4418
 
3.8%
Other values (18)26881
23.2%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1708305.047
Minimum20000
Maximum180000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:34.937832image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile25000
Q1100000
median300000
Q3800000
95-th percentile7000000
Maximum180000000
Range179980000
Interquartile range (IQR)700000

Descriptive statistics

Standard deviation6765265.327
Coefficient of variation (CV)3.960220887
Kurtosis177.9158546
Mean1708305.047
Median Absolute Deviation (MAD)225000
Skewness11.03487257
Sum1.519879 × 1010
Variance4.576881494 × 1013
MonotonicityNot monotonic
2022-05-29T23:01:35.201741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000689
 
7.7%
50000658
 
7.4%
200000537
 
6.0%
25000520
 
5.8%
150000514
 
5.8%
300000485
 
5.5%
250000448
 
5.0%
400000424
 
4.8%
75000398
 
4.5%
500000375
 
4.2%
Other values (112)3849
43.3%
ValueCountFrequency (%)
200002
 
< 0.1%
25000520
5.8%
300001
 
< 0.1%
50000658
7.4%
75000398
4.5%
100000689
7.7%
1200001
 
< 0.1%
125000203
 
2.3%
150000514
5.8%
175000128
 
1.4%
ValueCountFrequency (%)
1800000001
 
< 0.1%
1500000002
 
< 0.1%
1400000001
 
< 0.1%
1000000003
 
< 0.1%
800000006
0.1%
750000003
 
< 0.1%
700000006
0.1%
650000007
0.1%
600000004
< 0.1%
500000008
0.1%

goal_champ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct133
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.68967068
Minimum0
Maximum423
Zeros3009
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:35.424896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q316
95-th percentile49
Maximum423
Range423
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.8208499
Coefficient of variation (CV)1.610041071
Kurtosis36.14410883
Mean11.68967068
Median Absolute Deviation (MAD)4
Skewness3.778256906
Sum104003
Variance354.2243908
MonotonicityNot monotonic
2022-05-29T23:01:35.647910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03009
33.8%
1551
 
6.2%
3407
 
4.6%
2377
 
4.2%
4313
 
3.5%
5251
 
2.8%
6248
 
2.8%
7229
 
2.6%
10183
 
2.1%
8179
 
2.0%
Other values (123)3150
35.4%
ValueCountFrequency (%)
03009
33.8%
1551
 
6.2%
2377
 
4.2%
3407
 
4.6%
4313
 
3.5%
5251
 
2.8%
6248
 
2.8%
7229
 
2.6%
8179
 
2.0%
9162
 
1.8%
ValueCountFrequency (%)
4231
< 0.1%
2221
< 0.1%
1901
< 0.1%
1811
< 0.1%
1601
< 0.1%
1561
< 0.1%
1521
< 0.1%
1491
< 0.1%
1391
< 0.1%
1371
< 0.1%

assist_champ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct111
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.137574463
Minimum0
Maximum174
Zeros3280
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:35.900281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile39
Maximum174
Range174
Interquartile range (IQR)12

Descriptive statistics

Standard deviation15.09760514
Coefficient of variation (CV)1.652255223
Kurtosis15.45068298
Mean9.137574463
Median Absolute Deviation (MAD)3
Skewness3.155139646
Sum81297
Variance227.9376809
MonotonicityNot monotonic
2022-05-29T23:01:36.151769image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03280
36.9%
1630
 
7.1%
2465
 
5.2%
3400
 
4.5%
5306
 
3.4%
4288
 
3.2%
6262
 
2.9%
7216
 
2.4%
8207
 
2.3%
9206
 
2.3%
Other values (101)2637
29.6%
ValueCountFrequency (%)
03280
36.9%
1630
 
7.1%
2465
 
5.2%
3400
 
4.5%
4288
 
3.2%
5306
 
3.4%
6262
 
2.9%
7216
 
2.4%
8207
 
2.3%
9206
 
2.3%
ValueCountFrequency (%)
1741
< 0.1%
1601
< 0.1%
1571
< 0.1%
1542
< 0.1%
1511
< 0.1%
1471
< 0.1%
1391
< 0.1%
1242
< 0.1%
1221
< 0.1%
1181
< 0.1%

own_goal_champ
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0990221423
Minimum0
Maximum6
Zeros8157
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:36.316634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3613774937
Coefficient of variation (CV)3.649461477
Kurtosis34.56223224
Mean0.0990221423
Median Absolute Deviation (MAD)0
Skewness4.815458941
Sum881
Variance0.130593693
MonotonicityNot monotonic
2022-05-29T23:01:36.507530image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08157
91.7%
1628
 
7.1%
291
 
1.0%
317
 
0.2%
42
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
08157
91.7%
1628
 
7.1%
291
 
1.0%
317
 
0.2%
42
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
42
 
< 0.1%
317
 
0.2%
291
 
1.0%
1628
 
7.1%
08157
91.7%

yellow_card_champ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct115
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.80645161
Minimum0
Maximum142
Zeros1303
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:36.708559image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q321
95-th percentile52
Maximum142
Range142
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.82819408
Coefficient of variation (CV)1.204082825
Kurtosis5.116193167
Mean14.80645161
Median Absolute Deviation (MAD)8
Skewness2.02113267
Sum131733
Variance317.8445042
MonotonicityNot monotonic
2022-05-29T23:01:36.937912image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01303
 
14.6%
1624
 
7.0%
2519
 
5.8%
3386
 
4.3%
4366
 
4.1%
7334
 
3.8%
5329
 
3.7%
6322
 
3.6%
8262
 
2.9%
9243
 
2.7%
Other values (105)4209
47.3%
ValueCountFrequency (%)
01303
14.6%
1624
7.0%
2519
 
5.8%
3386
 
4.3%
4366
 
4.1%
5329
 
3.7%
6322
 
3.6%
7334
 
3.8%
8262
 
2.9%
9243
 
2.7%
ValueCountFrequency (%)
1421
 
< 0.1%
1411
 
< 0.1%
1233
< 0.1%
1221
 
< 0.1%
1171
 
< 0.1%
1151
 
< 0.1%
1142
< 0.1%
1111
 
< 0.1%
1091
 
< 0.1%
1082
< 0.1%

second_yellow_card_champ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4251995055
Minimum0
Maximum9
Zeros6616
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:37.117470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9188321411
Coefficient of variation (CV)2.160943579
Kurtosis14.19919891
Mean0.4251995055
Median Absolute Deviation (MAD)0
Skewness3.205350763
Sum3783
Variance0.8442525035
MonotonicityNot monotonic
2022-05-29T23:01:37.235206image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
06616
74.4%
11424
 
16.0%
2494
 
5.6%
3211
 
2.4%
486
 
1.0%
532
 
0.4%
616
 
0.2%
79
 
0.1%
86
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
06616
74.4%
11424
 
16.0%
2494
 
5.6%
3211
 
2.4%
486
 
1.0%
532
 
0.4%
616
 
0.2%
79
 
0.1%
86
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
93
 
< 0.1%
86
 
0.1%
79
 
0.1%
616
 
0.2%
532
 
0.4%
486
 
1.0%
3211
 
2.4%
2494
 
5.6%
11424
 
16.0%
06616
74.4%

red_card_champ
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4077779027
Minimum0
Maximum8
Zeros6511
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:37.403208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8193768036
Coefficient of variation (CV)2.009370293
Kurtosis9.264258729
Mean0.4077779027
Median Absolute Deviation (MAD)0
Skewness2.687325842
Sum3628
Variance0.6713783464
MonotonicityNot monotonic
2022-05-29T23:01:37.596163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
06511
73.2%
11582
 
17.8%
2530
 
6.0%
3161
 
1.8%
474
 
0.8%
531
 
0.3%
65
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
06511
73.2%
11582
 
17.8%
2530
 
6.0%
3161
 
1.8%
474
 
0.8%
531
 
0.3%
65
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
72
 
< 0.1%
65
 
0.1%
531
 
0.3%
474
 
0.8%
3161
 
1.8%
2530
 
6.0%
11582
 
17.8%
06511
73.2%

goal_cup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8764752164
Minimum0
Maximum44
Zeros6443
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:37.785112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum44
Range44
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.217116983
Coefficient of variation (CV)2.529583201
Kurtosis40.94090027
Mean0.8764752164
Median Absolute Deviation (MAD)0
Skewness4.922536757
Sum7798
Variance4.915607718
MonotonicityNot monotonic
2022-05-29T23:01:37.960833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
06443
72.4%
1930
 
10.5%
2501
 
5.6%
3308
 
3.5%
4188
 
2.1%
5151
 
1.7%
6112
 
1.3%
765
 
0.7%
842
 
0.5%
933
 
0.4%
Other values (17)124
 
1.4%
ValueCountFrequency (%)
06443
72.4%
1930
 
10.5%
2501
 
5.6%
3308
 
3.5%
4188
 
2.1%
5151
 
1.7%
6112
 
1.3%
765
 
0.7%
842
 
0.5%
933
 
0.4%
ValueCountFrequency (%)
441
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
243
< 0.1%
213
< 0.1%
201
 
< 0.1%
195
0.1%
182
 
< 0.1%
172
 
< 0.1%

assist_cup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7146229066
Minimum0
Maximum23
Zeros6662
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:38.071479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.890684625
Coefficient of variation (CV)2.645709516
Kurtosis28.18207101
Mean0.7146229066
Median Absolute Deviation (MAD)0
Skewness4.546469487
Sum6358
Variance3.57468835
MonotonicityNot monotonic
2022-05-29T23:01:38.192082image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
06662
74.9%
1990
 
11.1%
2442
 
5.0%
3253
 
2.8%
4161
 
1.8%
595
 
1.1%
680
 
0.9%
748
 
0.5%
945
 
0.5%
842
 
0.5%
Other values (12)79
 
0.9%
ValueCountFrequency (%)
06662
74.9%
1990
 
11.1%
2442
 
5.0%
3253
 
2.8%
4161
 
1.8%
595
 
1.1%
680
 
0.9%
748
 
0.5%
842
 
0.5%
945
 
0.5%
ValueCountFrequency (%)
232
 
< 0.1%
222
 
< 0.1%
202
 
< 0.1%
185
 
0.1%
171
 
< 0.1%
165
 
0.1%
154
 
< 0.1%
145
 
0.1%
139
0.1%
1216
0.2%

own_goal_cup
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
0.0
8745 
1.0
 
144
2.0
 
6
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26.691
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08745
98.3%
1.0144
 
1.6%
2.06
 
0.1%
3.02
 
< 0.1%

Length

2022-05-29T23:01:38.320191image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T23:01:38.535952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08745
98.3%
1.0144
 
1.6%
2.06
 
0.1%
3.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
017642
66.1%
.8897
33.3%
1144
 
0.5%
26
 
< 0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17794
66.7%
Other Punctuation8897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017642
99.1%
1144
 
0.8%
26
 
< 0.1%
32
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.8897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017642
66.1%
.8897
33.3%
1144
 
0.5%
26
 
< 0.1%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017642
66.1%
.8897
33.3%
1144
 
0.5%
26
 
< 0.1%
32
 
< 0.1%

yellow_card_cup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.862762729
Minimum0
Maximum26
Zeros6189
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:38.731438image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.99942194
Coefficient of variation (CV)2.317464435
Kurtosis23.98229657
Mean0.862762729
Median Absolute Deviation (MAD)0
Skewness4.102047419
Sum7676
Variance3.997688095
MonotonicityNot monotonic
2022-05-29T23:01:38.930845image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
06189
69.6%
11121
 
12.6%
2567
 
6.4%
3366
 
4.1%
4195
 
2.2%
5122
 
1.4%
6104
 
1.2%
763
 
0.7%
845
 
0.5%
931
 
0.3%
Other values (14)94
 
1.1%
ValueCountFrequency (%)
06189
69.6%
11121
 
12.6%
2567
 
6.4%
3366
 
4.1%
4195
 
2.2%
5122
 
1.4%
6104
 
1.2%
763
 
0.7%
845
 
0.5%
931
 
0.3%
ValueCountFrequency (%)
261
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
193
 
< 0.1%
183
 
< 0.1%
171
 
< 0.1%
169
0.1%
154
< 0.1%
149
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
0.0
8646 
1.0
 
239
2.0
 
9
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26.691
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08646
97.2%
1.0239
 
2.7%
2.09
 
0.1%
3.03
 
< 0.1%

Length

2022-05-29T23:01:39.137335image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T23:01:39.310798image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08646
97.2%
1.0239
 
2.7%
2.09
 
0.1%
3.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
017543
65.7%
.8897
33.3%
1239
 
0.9%
29
 
< 0.1%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17794
66.7%
Other Punctuation8897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017543
98.6%
1239
 
1.3%
29
 
0.1%
33
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.8897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017543
65.7%
.8897
33.3%
1239
 
0.9%
29
 
< 0.1%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017543
65.7%
.8897
33.3%
1239
 
0.9%
29
 
< 0.1%
33
 
< 0.1%

red_card_cup
Real number (ℝ≥0)

ZEROS

Distinct68
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.362818928
Minimum0
Maximum107
Zeros7803
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:39.519767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.947847577
Coefficient of variation (CV)4.364371125
Kurtosis68.99345014
Mean1.362818928
Median Absolute Deviation (MAD)0
Skewness7.136114243
Sum12125
Variance35.37689079
MonotonicityNot monotonic
2022-05-29T23:01:39.795470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07803
87.7%
1245
 
2.8%
285
 
1.0%
377
 
0.9%
553
 
0.6%
452
 
0.6%
641
 
0.5%
841
 
0.5%
940
 
0.4%
737
 
0.4%
Other values (58)423
 
4.8%
ValueCountFrequency (%)
07803
87.7%
1245
 
2.8%
285
 
1.0%
377
 
0.9%
452
 
0.6%
553
 
0.6%
641
 
0.5%
737
 
0.4%
841
 
0.5%
940
 
0.4%
ValueCountFrequency (%)
1071
< 0.1%
1021
< 0.1%
891
< 0.1%
801
< 0.1%
781
< 0.1%
771
< 0.1%
752
< 0.1%
701
< 0.1%
691
< 0.1%
651
< 0.1%

goal_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4982578397
Minimum0
Maximum121
Zeros7581
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:40.012580image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum121
Range121
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.27232685
Coefficient of variation (CV)4.560544097
Kurtosis943.6799231
Mean0.4982578397
Median Absolute Deviation (MAD)0
Skewness21.52801949
Sum4433
Variance5.163469312
MonotonicityNot monotonic
2022-05-29T23:01:40.201980image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
07581
85.2%
1542
 
6.1%
2240
 
2.7%
3159
 
1.8%
4101
 
1.1%
560
 
0.7%
654
 
0.6%
736
 
0.4%
824
 
0.3%
923
 
0.3%
Other values (20)77
 
0.9%
ValueCountFrequency (%)
07581
85.2%
1542
 
6.1%
2240
 
2.7%
3159
 
1.8%
4101
 
1.1%
560
 
0.7%
654
 
0.6%
736
 
0.4%
824
 
0.3%
923
 
0.3%
ValueCountFrequency (%)
1211
< 0.1%
461
< 0.1%
421
< 0.1%
391
< 0.1%
261
< 0.1%
241
< 0.1%
231
< 0.1%
222
< 0.1%
211
< 0.1%
201
< 0.1%

assist_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4761155446
Minimum0
Maximum44
Zeros7608
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:40.385484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum44
Range44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.900254815
Coefficient of variation (CV)3.991163147
Kurtosis114.3346353
Mean0.4761155446
Median Absolute Deviation (MAD)0
Skewness8.642728754
Sum4236
Variance3.610968363
MonotonicityNot monotonic
2022-05-29T23:01:40.603059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
07608
85.5%
1513
 
5.8%
2261
 
2.9%
3147
 
1.7%
4109
 
1.2%
564
 
0.7%
652
 
0.6%
732
 
0.4%
822
 
0.2%
917
 
0.2%
Other values (19)72
 
0.8%
ValueCountFrequency (%)
07608
85.5%
1513
 
5.8%
2261
 
2.9%
3147
 
1.7%
4109
 
1.2%
564
 
0.7%
652
 
0.6%
732
 
0.4%
822
 
0.2%
917
 
0.2%
ValueCountFrequency (%)
441
 
< 0.1%
391
 
< 0.1%
341
 
< 0.1%
293
< 0.1%
281
 
< 0.1%
261
 
< 0.1%
232
< 0.1%
222
< 0.1%
212
< 0.1%
201
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
0.0
8830 
1.0
 
61
2.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26.691
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08830
99.2%
1.061
 
0.7%
2.06
 
0.1%

Length

2022-05-29T23:01:40.783785image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T23:01:41.039763image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0.08830
99.2%
1.061
 
0.7%
2.06
 
0.1%

Most occurring characters

ValueCountFrequency (%)
017727
66.4%
.8897
33.3%
161
 
0.2%
26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17794
66.7%
Other Punctuation8897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017727
99.6%
161
 
0.3%
26
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.8897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017727
66.4%
.8897
33.3%
161
 
0.2%
26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017727
66.4%
.8897
33.3%
161
 
0.2%
26
 
< 0.1%

yellow_card_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6123412386
Minimum0
Maximum28
Zeros7234
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:41.247727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.915303199
Coefficient of variation (CV)3.127836374
Kurtosis40.44595474
Mean0.6123412386
Median Absolute Deviation (MAD)0
Skewness5.395206448
Sum5448
Variance3.668386346
MonotonicityNot monotonic
2022-05-29T23:01:42.636878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
07234
81.3%
1592
 
6.7%
2370
 
4.2%
3217
 
2.4%
4125
 
1.4%
587
 
1.0%
663
 
0.7%
750
 
0.6%
842
 
0.5%
928
 
0.3%
Other values (16)89
 
1.0%
ValueCountFrequency (%)
07234
81.3%
1592
 
6.7%
2370
 
4.2%
3217
 
2.4%
4125
 
1.4%
587
 
1.0%
663
 
0.7%
750
 
0.6%
842
 
0.5%
928
 
0.3%
ValueCountFrequency (%)
281
 
< 0.1%
252
< 0.1%
241
 
< 0.1%
232
< 0.1%
211
 
< 0.1%
204
< 0.1%
193
< 0.1%
181
 
< 0.1%
172
< 0.1%
163
< 0.1%

second_yellow_card_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9870742947
Minimum0
Maximum195
Zeros8285
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2022-05-29T23:01:43.000583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum195
Range195
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.40880543
Coefficient of variation (CV)6.492728526
Kurtosis197.603344
Mean0.9870742947
Median Absolute Deviation (MAD)0
Skewness11.62027397
Sum8782
Variance41.07278704
MonotonicityNot monotonic
2022-05-29T23:01:43.453392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08285
93.1%
1146
 
1.6%
244
 
0.5%
332
 
0.4%
631
 
0.3%
727
 
0.3%
524
 
0.3%
423
 
0.3%
820
 
0.2%
1318
 
0.2%
Other values (62)247
 
2.8%
ValueCountFrequency (%)
08285
93.1%
1146
 
1.6%
244
 
0.5%
332
 
0.4%
423
 
0.3%
524
 
0.3%
631
 
0.3%
727
 
0.3%
820
 
0.2%
917
 
0.2%
ValueCountFrequency (%)
1951
< 0.1%
1221
< 0.1%
1201
< 0.1%
1161
< 0.1%
1051
< 0.1%
1021
< 0.1%
991
< 0.1%
942
< 0.1%
861
< 0.1%
841
< 0.1%

Interactions

2022-05-29T23:01:26.991782image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:17.264193image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-05-29T23:00:24.590879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:29.170152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:32.281752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:36.128361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:39.827898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:44.320513image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-05-29T23:01:16.248103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:19.423643image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:22.431042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:26.695392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:30.298649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:20.475058image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:24.408398image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:28.758618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:32.147746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:35.947623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:39.645008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:44.147123image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:48.542805image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:52.384969image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:55.801011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:00:59.476689image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:02.886957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:06.347302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:10.094174image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:13.363455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:16.417048image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:19.586825image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:22.596465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-05-29T23:01:26.860058image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-05-29T23:01:43.868254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-29T23:01:44.394922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-29T23:01:44.723660image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-29T23:01:44.991470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-29T23:01:45.242889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-29T23:01:30.683723image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-29T23:01:31.254754image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0red_card_continentagenationleagueteamgoals_selectionselections_nationpositionpricegoal_champassist_champown_goal_champyellow_card_champsecond_yellow_card_champred_card_champgoal_cupassist_cupown_goal_cupyellow_card_cupsecond_yellow_card_cupred_card_cupgoal_continentassist_continentown_goal_continentyellow_card_continentsecond_yellow_card_continent
010190.023TunisiaLigue1OGCNice04Goalkeeper1000000.00.00.00.05.00.01.00.00.00.00.00.012.00.00.00.00.00.0
118920.020CzechRepublicFortunaLiga1.FKPribram01LeftWinger100000.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.0
225487.031CanadaSuperLigaRedStar040Goalkeeper3000000.00.00.01.031.01.00.00.00.00.00.00.023.00.00.00.00.025.0
331370.022BelgiumJupilerProLeagueClubBrugge418SecondStriker4000000.033.022.00.018.00.00.00.03.00.02.00.00.00.01.00.02.00.0
425760.029UkrainePremierLigaChornomorets00LeftMidfield500000.020.019.00.034.03.01.00.00.00.00.00.00.00.00.00.00.00.0
5121250.025JapanJ1LeagueMatsumotoYama.00LeftMidfield200000.02.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.0
633121.031ChileSüperLigBesiktas7116DefensiveMidfield4500000.014.011.00.086.06.03.04.04.00.011.01.00.01.01.00.011.01.0
7112330.032IranPersianGulfProLeagueParsJam00RightWinger100000.016.07.00.015.00.01.00.01.00.01.00.01.00.00.00.00.00.0
892270.034ArgentinaPrimeraDivisiónBocaJuniors1376SecondStriker1000000.0149.074.00.042.00.01.026.08.00.06.00.00.022.010.00.016.00.0
947920.021ItalySerieASSCNapoli00Goalkeeper16000000.00.00.00.02.00.00.00.00.00.00.00.010.00.00.00.00.00.0

Last rows

Unnamed: 0red_card_continentagenationleagueteamgoals_selectionselections_nationpositionpricegoal_champassist_champown_goal_champyellow_card_champsecond_yellow_card_champred_card_champgoal_cupassist_cupown_goal_cupyellow_card_cupsecond_yellow_card_cupred_card_cupgoal_continentassist_continentown_goal_continentyellow_card_continentsecond_yellow_card_continent
888785200.020CanadaMLSVancouver03CentralMidfield175000.01.03.00.011.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8888118820.022MyanmarThaiLeagueMuangthongUtd.824SecondStriker350000.017.08.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.0
888971550.025MexicoAscensoMXClausuraCelaya00Forward700000.04.02.00.010.00.00.00.00.00.00.00.00.00.00.00.00.00.0
8890100430.025UzbekistanSuperligaPakhtakor217LeftWinger1400000.010.01.00.08.00.00.00.00.00.00.00.00.02.03.00.00.00.0
889137060.029RussiaPremierLigaKrasnodar01RightMidfield3500000.021.034.02.046.01.00.00.00.00.00.00.00.00.00.00.01.00.0
889236590.024RussiaPremierLigaArsenalTula00Goalkeeper125000.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.0
889390630.024UruguayPrimeraDivisiónArgentinos00AttackingMidfield300000.012.010.00.011.02.01.02.01.00.03.00.00.00.00.00.00.00.0
889491630.029ArgentinaPrimeraDivisiónDefensa011Goalkeeper800000.00.00.00.06.00.00.00.00.00.00.00.036.00.00.00.00.00.0
889529800.029ArgentinaSuperLeagueApollonSmyrnis12AttackingMidfield300000.019.029.00.020.02.00.00.01.00.01.00.00.00.01.00.00.00.0
8896103680.030ChinaLeagueOneYBFunde00LeftMidfield175000.015.05.00.07.01.00.00.00.00.00.00.00.00.00.00.00.00.0